For certain age groups, such as the elderly, or people that engage in certain dangerous activities, such as firefighters and soldiers, a fall can adversely affect health. As a result, many fall detection systems and devices have been developed. Many such systems and devices employ accelerometers that measure sudden changes in acceleration that may indicate a fall, such as rapid changes in acceleration followed by no movement (i.e., lying on the floor). Such methods have difficulty distinguishing falls from activities of daily living (ADL). This makes it difficult to distinguish real falls from certain fall-like activities such as sitting or lying down quickly, resulting in many false positives. Body orientation is also used as a means of detecting falls, but it is not very useful when the ending position is not horizontal, e.g., falls happening on stairs.
U.S. Patent Application Publication No. US 2006/0279426 A1 (hereinafter “the '426 publication”) describes a device which includes a user-worn accelerometer and magnetometer that assumes a person is in a standing position. A fall event is declared when a significant and rapid acceleration signal coincides with a shift in ambient magnetic fields between two levels. However, the device of the '426 publication requires complicated algorithms to remove false positives and negatives, and is therefore computationally expensive, power hungry, and produces uncertain results.
A paper by Q. Li, et al., titled, “Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information,” College of William and Mary, (hereinafter “Li et al.”) describes a system and method for detecting falls that employs gyroscopes in addition to accelerometers. In Li et al., human activities are divided into two categories: static postures and dynamic transitions. By using two tri-axial accelerometers at separate body locations, the system can recognize four kinds of static postures: standing, bending, sitting, and lying. Motions between these static postures are considered to be dynamic transitions. Linear acceleration and angular velocity are measured to determine whether motion transitions are intentional. If the transition before a lying posture is not intentional, a fall event is declared.
The system of Li et al. requires sensors to be distributed in several locations, which is not convenient for a user nor is it practical to implement when the user is in an unfamiliar environment. Moreover, continuous monitoring with gyroscopes requires a large amount of power.
Another conventional solution includes a stationary device that bases fall decisions on measurements of floor vibrations and audio analysis. To enable a user to have an audio conversation using a mobile version of the aforementioned stationary device, two microphones are employed to remove background noise. This solution arbitrarily designates one microphone to be the primary microphone for measurements and the other microphone is employed for detecting the background noise. This renders it difficult to distinguish between human activity and other sources of noise vibration, such as an object falling off a table.